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An image registration method for colposcopic images.

Mezura-Montes E, Acosta-Mesa HG, Ramírez-Garcés DD, Cruz-Ramírez N, Hernández-Jiménez R - Comput Math Methods Med (2013)

Bottom Line: The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window.The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature.The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Artificial Intelligence, University of Veracruz, Sebastián Camacho 5, 91000 Centro Xalapa, VER, Mexico.

ABSTRACT
A nonrigid body image registration method for spatiotemporal alignment of image sequences obtained from colposcopy examinations to detect precancerous lesions of the cervix is proposed in this paper. The approach is based on time series calculation for those pixels in the first image of the sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

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Related in: MedlinePlus

Time series computing of two pixels.
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Related In: Results  -  Collection


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fig2: Time series computing of two pixels.

Mentions: Figure 2 shows the acetowhite response functions, by following the change of the intensity of two pixels with coordinates (x1, y1) and (x2, y2), respectively, in the images sequence. The monitoring begins in the image 0 (i.e., reference image) and ends in image n, where z is the total number of images in the sequence (i.e., z represents the time). As a result, Figure 3 shows the time series plot of a pixel.


An image registration method for colposcopic images.

Mezura-Montes E, Acosta-Mesa HG, Ramírez-Garcés DD, Cruz-Ramírez N, Hernández-Jiménez R - Comput Math Methods Med (2013)

Time series computing of two pixels.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3794537&req=5

fig2: Time series computing of two pixels.
Mentions: Figure 2 shows the acetowhite response functions, by following the change of the intensity of two pixels with coordinates (x1, y1) and (x2, y2), respectively, in the images sequence. The monitoring begins in the image 0 (i.e., reference image) and ends in image n, where z is the total number of images in the sequence (i.e., z represents the time). As a result, Figure 3 shows the time series plot of a pixel.

Bottom Line: The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window.The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature.The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Artificial Intelligence, University of Veracruz, Sebastián Camacho 5, 91000 Centro Xalapa, VER, Mexico.

ABSTRACT
A nonrigid body image registration method for spatiotemporal alignment of image sequences obtained from colposcopy examinations to detect precancerous lesions of the cervix is proposed in this paper. The approach is based on time series calculation for those pixels in the first image of the sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

Show MeSH
Related in: MedlinePlus